Elon Musk's newly approved trillion-dollar pay package is less about the money and more about securing 25% voting control of Tesla. He views Tesla's future not in cars but in humanoid robots, and he sought this control to direct the development of this potentially world-changing technology.

Musk envisions a future where a fleet of 100 million Teslas, each with a kilowatt of inference compute, built-in power, cooling, and Wi-Fi, could be networked together. This would create a massive, distributed compute resource for AI tasks.

For a hyperscaler, the main benefit of designing a custom AI chip isn't necessarily superior performance, but gaining control. It allows them to escape the supply allocations dictated by NVIDIA and chart their own course, even if their chip is slightly less performant or more expensive to deploy.

In a significant shift, Elon Musk stated he now believes xAI has a chance to achieve AGI with its fifth-generation model, Grok 5. Coming from a key player who is rapidly scaling compute, this suggests the timeline for world-changing AI could be within the next few years.

Tesla's decision to stop developing its Dojo training supercomputer is not a failure. It's a strategic shift to focus on designing hyper-efficient inference chips for its vehicles and robots. This vertical integration at the edge, where real-world decisions are made, is seen as more critical than competing with NVIDIA on training hardware.

Musk's decisions—choosing cameras over LiDAR for Tesla and acquiring X (Twitter)—are part of a unified strategy to own the largest data sets of real-world patterns (driving and human behavior). This allows him to train and perfect AI, making his companies data juggernauts.

Initially criticized for forgoing expensive LIDAR, Tesla's vision-based self-driving system compelled it to solve the harder, more scalable problem of AI-based reasoning. This long-term bet on foundation models for driving is now converging with the direction competitors are also taking.

Tesla's latest master plan signals a philosophical pivot from mere sustainability to 'sustainable abundance.' The new vision is to leverage AI, automation, and manufacturing scale to overcome fundamental societal constraints in energy, labor, and resources, rejecting a zero-sum view of growth.

The current 2-3 year chip design cycle is a major bottleneck for AI progress, as hardware is always chasing outdated software needs. By using AI to slash this timeline, companies can enable a massive expansion of custom chips, optimizing performance for many at-scale software workloads.